Contour detection model using linear and non‐linear modulation based on non‐CRF suppression. Issue 6 (1st June 2018)

• Record Type: Journal Article
• Authors: Lin, Chuan; Xu, Guili; Cao, Yijun
• Is Part Of: IET image processing. Volume 12:Issue 6(2018)
• Page Start: 993
• Page End: 1003
• Publication Date: 2018-06-01
• Subjects: edge detection -- modulation -- image texture; contour detection model -- nonlinear modulation -- linear modulation -- nonCRF suppression -- neurophysiological investigations -- psychophysical investigations -- human visual system -- primary visual cortex -- nonclassical receptive field region -- CRF region -- suppression mechanism -- inhibition term -- texture suppression -- contour protection -- spatial summation properties -- retinal X cells -- visual information processing -- X‐Y channel -- contour detector -- machine vision; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0679
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Psychophysical and neurophysiological investigations on the human visual system show that most neurons in the primary visual cortex (V1) possess a non‐classical receptive field (nCRF) region in addition to the CRF region. The nCRF has a modulatory, normally inhibitory, effect on the responses to visual stimuli generated within the CRF. In computational terms, this mechanism suppresses the response to edges in the presence of similar edges in the surroundings. Many computational techniques have been proposed to address the surround suppression mechanism. These methods introduce an inhibition term that is required to suppress the textures and protect the contours. Several studies have found that the spatial summation properties over the receptive fields of retinal X cells are approximately linear, while they are non‐linear for Y cells. Inspired by the visual information processing in the X–Y channel and spatial summation properties of X and Y cells, the authors propose a contour detector using linear and non‐linear modulations based on nCRF suppression. Extensive experimental evaluations demonstrate that their contour detector significantly outperforms other algorithms. The methods proposed in this study are expected to facilitate the development of efficient computational models in the field of machine vision.